Overview

Dataset statistics

Number of variables14
Number of observations9850
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory112.0 B

Variable types

Numeric11
Categorical3

Alerts

BirthYear is highly correlated with MonthSal and 1 other fieldsHigh correlation
MonthSal is highly correlated with BirthYearHigh correlation
Children is highly correlated with BirthYearHigh correlation
CustMonVal is highly correlated with ClaimsRateHigh correlation
ClaimsRate is highly correlated with CustMonValHigh correlation
PremMotor is highly correlated with PremHousehold and 3 other fieldsHigh correlation
PremHousehold is highly correlated with PremMotorHigh correlation
PremHealth is highly correlated with PremMotorHigh correlation
PremLife is highly correlated with PremMotorHigh correlation
PremWork is highly correlated with PremMotorHigh correlation
CustID is uniformly distributed Uniform
CustID has unique values Unique

Reproduction

Analysis started2022-01-07 17:26:33.528536
Analysis finished2022-01-07 17:26:47.938705
Duration14.41 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

CustID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct9850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5146.934518
Minimum1
Maximum10296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2022-01-07T17:26:48.033652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile519.45
Q12571.25
median5147.5
Q37713.75
95-th percentile9784.55
Maximum10296
Range10295
Interquartile range (IQR)5142.5

Descriptive statistics

Standard deviation2968.364203
Coefficient of variation (CV)0.5767246878
Kurtosis-1.197924516
Mean5146.934518
Median Absolute Deviation (MAD)2572
Skewness0.002176056462
Sum50697305
Variance8811186.041
MonotonicityStrictly increasing
2022-01-07T17:26:48.174512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20491
 
< 0.1%
27561
 
< 0.1%
47911
 
< 0.1%
88891
 
< 0.1%
27481
 
< 0.1%
7011
 
< 0.1%
68461
 
< 0.1%
47991
 
< 0.1%
88971
 
< 0.1%
7091
 
< 0.1%
Other values (9840)9840
99.9%
ValueCountFrequency (%)
11
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
102961
< 0.1%
102951
< 0.1%
102941
< 0.1%
102921
< 0.1%
102901
< 0.1%
102891
< 0.1%
102881
< 0.1%
102871
< 0.1%
102861
< 0.1%
102851
< 0.1%

FirstPolYear
Real number (ℝ≥0)

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.007614
Minimum1974
Maximum1998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2022-01-07T17:26:48.289645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1976
Q11980
median1986
Q31992
95-th percentile1996
Maximum1998
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.585369078
Coefficient of variation (CV)0.003315883097
Kurtosis-1.155080145
Mean1986.007614
Median Absolute Deviation (MAD)6
Skewness-0.02094014126
Sum19562175
Variance43.36708589
MonotonicityNot monotonic
2022-01-07T17:26:48.387657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1988488
 
5.0%
1986469
 
4.8%
1994456
 
4.6%
1993454
 
4.6%
1984447
 
4.5%
1989443
 
4.5%
1977433
 
4.4%
1982433
 
4.4%
1992428
 
4.3%
1990427
 
4.3%
Other values (15)5372
54.5%
ValueCountFrequency (%)
1974133
 
1.4%
1975271
2.8%
1976415
4.2%
1977433
4.4%
1978427
4.3%
1979422
4.3%
1980415
4.2%
1981422
4.3%
1982433
4.4%
1983413
4.2%
ValueCountFrequency (%)
1998103
 
1.0%
1997248
2.5%
1996426
4.3%
1995424
4.3%
1994456
4.6%
1993454
4.6%
1992428
4.3%
1991418
4.2%
1990427
4.3%
1989443
4.5%

BirthYear
Real number (ℝ≥0)

HIGH CORRELATION

Distinct67
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1967.237563
Minimum1935
Maximum2001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2022-01-07T17:26:48.499114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1935
5-th percentile1941
Q11953
median1967
Q31981
95-th percentile1994
Maximum2001
Range66
Interquartile range (IQR)28

Descriptive statistics

Standard deviation16.89071892
Coefficient of variation (CV)0.008586008742
Kurtosis-1.129628226
Mean1967.237563
Median Absolute Deviation (MAD)14
Skewness0.02629900469
Sum19377290
Variance285.2963856
MonotonicityNot monotonic
2022-01-07T17:26:48.652793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1968215
 
2.2%
1962204
 
2.1%
1964193
 
2.0%
1953192
 
1.9%
1974187
 
1.9%
1981186
 
1.9%
1951185
 
1.9%
1977185
 
1.9%
1984184
 
1.9%
1963183
 
1.9%
Other values (57)7936
80.6%
ValueCountFrequency (%)
193514
 
0.1%
193636
 
0.4%
193756
 
0.6%
193875
0.8%
1939100
1.0%
1940127
1.3%
1941145
1.5%
1942160
1.6%
1943156
1.6%
1944168
1.7%
ValueCountFrequency (%)
20016
 
0.1%
200015
 
0.2%
199939
 
0.4%
199857
 
0.6%
199793
0.9%
199694
1.0%
1995109
1.1%
1994131
1.3%
1993130
1.3%
1992150
1.5%

EducDeg
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.1 KiB
b'3 - BSc/MSc'
4772 
b'2 - High School'
3370 
b'1 - Basic'
1012 
b'4 - PhD'
696 

Length

Max length18
Median length14
Mean length14.88040609
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb'2 - High School'
2nd rowb'1 - Basic'
3rd rowb'3 - BSc/MSc'
4th rowb'3 - BSc/MSc'
5th rowb'2 - High School'

Common Values

ValueCountFrequency (%)
b'3 - BSc/MSc'4772
48.4%
b'2 - High School'3370
34.2%
b'1 - Basic'1012
 
10.3%
b'4 - PhD'696
 
7.1%

Length

2022-01-07T17:26:48.778135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T17:26:48.850551image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
9850
29.9%
b'34772
14.5%
bsc/msc4772
14.5%
b'23370
 
10.2%
high3370
 
10.2%
school3370
 
10.2%
b'11012
 
3.1%
basic1012
 
3.1%
b'4696
 
2.1%
phd696
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MonthSal
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3481
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2543.724365
Minimum333
Maximum5021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2022-01-07T17:26:48.940856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum333
5-th percentile1005
Q11784
median2542.5
Q33307
95-th percentile4051.55
Maximum5021
Range4688
Interquartile range (IQR)1523

Descriptive statistics

Standard deviation959.9554771
Coefficient of variation (CV)0.3773818776
Kurtosis-0.8725865868
Mean2543.724365
Median Absolute Deviation (MAD)762
Skewness-0.008129194926
Sum25055685
Variance921514.518
MonotonicityNot monotonic
2022-01-07T17:26:49.064730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2501.536
 
0.4%
320010
 
0.1%
356010
 
0.1%
377610
 
0.1%
268710
 
0.1%
23089
 
0.1%
19249
 
0.1%
20739
 
0.1%
17669
 
0.1%
29599
 
0.1%
Other values (3471)9729
98.8%
ValueCountFrequency (%)
3332
< 0.1%
3341
 
< 0.1%
3352
< 0.1%
3412
< 0.1%
3441
 
< 0.1%
3481
 
< 0.1%
3501
 
< 0.1%
3562
< 0.1%
3584
< 0.1%
3641
 
< 0.1%
ValueCountFrequency (%)
50211
 
< 0.1%
49951
 
< 0.1%
49041
 
< 0.1%
48971
 
< 0.1%
48833
< 0.1%
48721
 
< 0.1%
48691
 
< 0.1%
48572
< 0.1%
48431
 
< 0.1%
47971
 
< 0.1%

GeoLivArea
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.1 KiB
4
3966 
1
2923 
3
1965 
2
996 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
43966
40.3%
12923
29.7%
31965
19.9%
2996
 
10.1%

Length

2022-01-07T17:26:49.173759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T17:26:49.235549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
43966
40.3%
12923
29.7%
31965
19.9%
2996
 
10.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Children
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size77.1 KiB
1
6986 
0
2864 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
16986
70.9%
02864
29.1%

Length

2022-01-07T17:26:49.311239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-07T17:26:49.373681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
16986
70.9%
02864
29.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CustMonVal
Real number (ℝ)

HIGH CORRELATION

Distinct6721
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.3027929
Minimum-312.83
Maximum1121.54
Zeros2
Zeros (%)< 0.1%
Negative2635
Negative (%)26.8%
Memory size77.1 KiB
2022-01-07T17:26:49.451046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-312.83
5-th percentile-87.727
Q1-8.9875
median186.265
Q3396.9425
95-th percentile615.3015
Maximum1121.54
Range1434.37
Interquartile range (IQR)405.93

Descriptive statistics

Standard deviation242.6529295
Coefficient of variation (CV)1.137598464
Kurtosis-0.1400726593
Mean213.3027929
Median Absolute Deviation (MAD)200.595
Skewness0.5958189796
Sum2101032.51
Variance58880.44418
MonotonicityNot monotonic
2022-01-07T17:26:49.570257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-25261
 
2.6%
-3711
 
0.1%
-3111
 
0.1%
-3511
 
0.1%
-15.1110
 
0.1%
-12.3310
 
0.1%
-47.679
 
0.1%
-21.119
 
0.1%
-339
 
0.1%
-10.339
 
0.1%
Other values (6711)9500
96.4%
ValueCountFrequency (%)
-312.831
< 0.1%
-312.611
< 0.1%
-312.281
< 0.1%
-307.271
< 0.1%
-298.911
< 0.1%
-291.161
< 0.1%
-280.161
< 0.1%
-278.911
< 0.1%
-272.271
< 0.1%
-270.381
< 0.1%
ValueCountFrequency (%)
1121.541
< 0.1%
1113.781
< 0.1%
1109.761
< 0.1%
1105.421
< 0.1%
1103.431
< 0.1%
1094.441
< 0.1%
1094.111
< 0.1%
1092.881
< 0.1%
1090.991
< 0.1%
1087.531
< 0.1%

ClaimsRate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6796172589
Minimum0
Maximum1.62
Zeros51
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2022-01-07T17:26:49.697306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16
Q10.39
median0.72
Q30.98
95-th percentile1.09
Maximum1.62
Range1.62
Interquartile range (IQR)0.59

Descriptive statistics

Standard deviation0.3160823264
Coefficient of variation (CV)0.4650887279
Kurtosis-1.193398532
Mean0.6796172589
Median Absolute Deviation (MAD)0.28
Skewness-0.2513727061
Sum6694.23
Variance0.09990803707
MonotonicityNot monotonic
2022-01-07T17:26:49.823166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1444
 
4.5%
1.01208
 
2.1%
1.02197
 
2.0%
1.03191
 
1.9%
0.99189
 
1.9%
0.98170
 
1.7%
0.97145
 
1.5%
0.95140
 
1.4%
1.04138
 
1.4%
0.91134
 
1.4%
Other values (132)7894
80.1%
ValueCountFrequency (%)
051
0.5%
0.011
 
< 0.1%
0.032
 
< 0.1%
0.045
 
0.1%
0.054
 
< 0.1%
0.0619
 
0.2%
0.0712
 
0.1%
0.0835
0.4%
0.0935
0.4%
0.125
0.3%
ValueCountFrequency (%)
1.621
 
< 0.1%
1.541
 
< 0.1%
1.511
 
< 0.1%
1.491
 
< 0.1%
1.411
 
< 0.1%
1.391
 
< 0.1%
1.371
 
< 0.1%
1.361
 
< 0.1%
1.344
< 0.1%
1.331
 
< 0.1%

PremMotor
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1900
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.3858142
Minimum1.78
Maximum585.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size77.1 KiB
2022-01-07T17:26:50.189812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.78
5-th percentile84.2895
Q1206.15
median308.28
Q3411.41
95-th percentile515.54
Maximum585.22
Range583.44
Interquartile range (IQR)205.26

Descriptive statistics

Standard deviation132.2857049
Coefficient of variation (CV)0.4317618467
Kurtosis-0.8754528165
Mean306.3858142
Median Absolute Deviation (MAD)102.24
Skewness-0.07873390618
Sum3017900.27
Variance17499.50773
MonotonicityNot monotonic
2022-01-07T17:26:50.309124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298.6142
 
0.4%
398.7417
 
0.2%
361.2916
 
0.2%
246.4916
 
0.2%
206.1515
 
0.2%
279.6115
 
0.2%
409.5214
 
0.1%
346.5113
 
0.1%
381.9613
 
0.1%
269.9413
 
0.1%
Other values (1890)9676
98.2%
ValueCountFrequency (%)
1.781
< 0.1%
3.781
< 0.1%
4.781
< 0.1%
7.671
< 0.1%
8.672
< 0.1%
9.671
< 0.1%
11.781
< 0.1%
13.561
< 0.1%
13.671
< 0.1%
14.561
< 0.1%
ValueCountFrequency (%)
585.221
 
< 0.1%
581.331
 
< 0.1%
580.111
 
< 0.1%
578.331
 
< 0.1%
577.331
 
< 0.1%
576.331
 
< 0.1%
575.442
< 0.1%
574.441
 
< 0.1%
574.331
 
< 0.1%
569.553
< 0.1%

PremHousehold
Real number (ℝ)

HIGH CORRELATION

Distinct973
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.4878985
Minimum-75
Maximum1231.9
Zeros59
Zeros (%)0.6%
Negative1080
Negative (%)11.0%
Memory size77.1 KiB
2022-01-07T17:26:50.433395image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-75
5-th percentile-30.55
Q148.35
median127.8
Q3274.5
95-th percentile642.35
Maximum1231.9
Range1306.9
Interquartile range (IQR)226.15

Descriptive statistics

Standard deviation211.8883235
Coefficient of variation (CV)1.100787765
Kurtosis2.854263525
Mean192.4878985
Median Absolute Deviation (MAD)98.9
Skewness1.614183135
Sum1896005.8
Variance44896.66163
MonotonicityNot monotonic
2022-01-07T17:26:50.563459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.4561
 
0.6%
19.4560
 
0.6%
-45.5560
 
0.6%
059
 
0.6%
-30.5557
 
0.6%
-5.5556
 
0.6%
69.4554
 
0.5%
44.4553
 
0.5%
-40.5553
 
0.5%
34.4553
 
0.5%
Other values (963)9284
94.3%
ValueCountFrequency (%)
-7518
 
0.2%
-7033
0.3%
-6535
0.4%
-6031
0.3%
-5527
0.3%
-5044
0.4%
-45.5560
0.6%
-4536
0.4%
-40.5553
0.5%
-4033
0.3%
ValueCountFrequency (%)
1231.91
< 0.1%
12281
< 0.1%
1216.91
< 0.1%
1202.452
< 0.1%
1201.91
< 0.1%
11981
< 0.1%
1194.12
< 0.1%
1179.11
< 0.1%
1154.12
< 0.1%
1153.551
< 0.1%

PremHealth
Real number (ℝ)

HIGH CORRELATION

Distinct1001
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.6226091
Minimum-2.11
Maximum442.86
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size77.1 KiB
2022-01-07T17:26:50.702042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.11
5-th percentile54.9
Q1113.02
median164.03
Q3220.82
95-th percentile298.39
Maximum442.86
Range444.97
Interquartile range (IQR)107.8

Descriptive statistics

Standard deviation74.26283219
Coefficient of variation (CV)0.4378121087
Kurtosis-0.4292132297
Mean169.6226091
Median Absolute Deviation (MAD)53.9
Skewness0.2903046845
Sum1670782.7
Variance5514.968244
MonotonicityNot monotonic
2022-01-07T17:26:50.833064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162.8157
 
0.6%
130.4730
 
0.3%
159.1427
 
0.3%
178.727
 
0.3%
158.1426
 
0.3%
169.726
 
0.3%
219.9325
 
0.3%
136.5825
 
0.3%
146.3624
 
0.2%
157.0324
 
0.2%
Other values (991)9559
97.0%
ValueCountFrequency (%)
-2.111
 
< 0.1%
5.781
 
< 0.1%
7.781
 
< 0.1%
11.671
 
< 0.1%
12.671
 
< 0.1%
14.672
< 0.1%
15.561
 
< 0.1%
15.672
< 0.1%
16.564
< 0.1%
16.672
< 0.1%
ValueCountFrequency (%)
442.861
< 0.1%
440.861
< 0.1%
432.971
< 0.1%
417.31
< 0.1%
417.081
< 0.1%
408.411
< 0.1%
401.631
< 0.1%
398.411
< 0.1%
394.521
< 0.1%
393.741
< 0.1%

PremLife
Real number (ℝ)

HIGH CORRELATION

Distinct461
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.1919665
Minimum-7
Maximum183.48
Zeros0
Zeros (%)0.0%
Negative660
Negative (%)6.7%
Memory size77.1 KiB
2022-01-07T17:26:50.995619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-7
5-th percentile-1.11
Q19.89
median24.67
Q353.01
95-th percentile121.8
Maximum183.48
Range190.48
Interquartile range (IQR)43.12

Descriptive statistics

Standard deviation38.13965229
Coefficient of variation (CV)1.025480927
Kurtosis1.883385686
Mean37.1919665
Median Absolute Deviation (MAD)17.89
Skewness1.474881573
Sum366340.87
Variance1454.633077
MonotonicityNot monotonic
2022-01-07T17:26:51.115274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.56160
 
1.6%
9.89128
 
1.3%
3.89119
 
1.2%
0.89119
 
1.2%
-1.11116
 
1.2%
5.89106
 
1.1%
6.89106
 
1.1%
12.89105
 
1.1%
4.89104
 
1.1%
7.89101
 
1.0%
Other values (451)8686
88.2%
ValueCountFrequency (%)
-765
0.7%
-661
0.6%
-574
0.8%
-456
0.6%
-370
0.7%
-272
0.7%
-1.11116
1.2%
-154
0.5%
-0.1192
0.9%
0.89119
1.2%
ValueCountFrequency (%)
183.485
0.1%
182.72
 
< 0.1%
182.593
< 0.1%
182.484
< 0.1%
181.71
 
< 0.1%
181.593
< 0.1%
181.481
 
< 0.1%
180.593
< 0.1%
179.71
 
< 0.1%
179.592
 
< 0.1%

PremWork
Real number (ℝ)

HIGH CORRELATION

Distinct756
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.95146497
Minimum-12
Maximum194.59
Zeros0
Zeros (%)0.0%
Negative909
Negative (%)9.2%
Memory size77.1 KiB
2022-01-07T17:26:51.244914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-4
Q110
median25.34
Q352.65
95-th percentile119.91
Maximum194.59
Range206.59
Interquartile range (IQR)42.65

Descriptive statistics

Standard deviation38.56906683
Coefficient of variation (CV)1.043776393
Kurtosis2.194138574
Mean36.95146497
Median Absolute Deviation (MAD)18.56
Skewness1.508209591
Sum363971.93
Variance1487.572916
MonotonicityNot monotonic
2022-01-07T17:26:51.358711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.67122
 
1.2%
10.8974
 
0.8%
9.8971
 
0.7%
11.8968
 
0.7%
-5.1166
 
0.7%
-0.1165
 
0.7%
14.8964
 
0.6%
15.8963
 
0.6%
3.7863
 
0.6%
16.8963
 
0.6%
Other values (746)9131
92.7%
ValueCountFrequency (%)
-1234
0.3%
-1131
0.3%
-1030
0.3%
-928
0.3%
-850
0.5%
-739
0.4%
-6.1157
0.6%
-644
0.4%
-5.1166
0.7%
-548
0.5%
ValueCountFrequency (%)
194.591
 
< 0.1%
194.371
 
< 0.1%
194.261
 
< 0.1%
194.151
 
< 0.1%
192.373
< 0.1%
191.482
< 0.1%
191.373
< 0.1%
191.261
 
< 0.1%
189.591
 
< 0.1%
189.371
 
< 0.1%

Interactions

2022-01-07T17:26:46.495449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:33.938712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:35.294693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:37.034256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.363479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.481392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:40.738451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.858580image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.965683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.076835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.364177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.585697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:34.077886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:35.819311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:37.142653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.465482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.581473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:40.837350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.955076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.068258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.170548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.467159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.674069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:34.217830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:35.934867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:37.243500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.558975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.679759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:40.935697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.052643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.168927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.263942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.564456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.768194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:34.329273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.050235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:37.381745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.674009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.777810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.040592image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.156867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.272261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.361790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.668671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.855000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:34.453534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.190641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:37.507201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.778542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.870760image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.140087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.252873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.366026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.452478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.775057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.951225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:34.561467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.305355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:37.626904image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.885600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.975918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.242643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-01-07T17:26:43.468886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.571203image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.881699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-01-07T17:26:34.679886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.436122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-01-07T17:26:41.351147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.464337image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.577322image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.676661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.993320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:47.148599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:34.787951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.550775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:37.884443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-01-07T17:26:40.343595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2022-01-07T17:26:42.566249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.678231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.774687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.098595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:47.244667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:34.901672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.704814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.012924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.194850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:40.455359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.555073image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.668051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.779075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.872374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.202807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:47.333445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:35.047951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.818775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.114382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.290074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:40.546768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.653325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.762567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.876841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:44.966953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.300343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:47.429357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:35.193286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:36.930343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:38.237226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:39.390443image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:40.646865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:41.761515image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:42.865373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:43.983084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:45.261509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-01-07T17:26:46.402743image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-01-07T17:26:51.469503image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Missing values

2022-01-07T17:26:47.597175image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-07T17:26:47.840366image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CustIDFirstPolYearBirthYearEducDegMonthSalGeoLivAreaChildrenCustMonValClaimsRatePremMotorPremHouseholdPremHealthPremLifePremWork
0119851982b'2 - High School'2177.011380.970.39375.8579.45146.3647.0116.89
1319911970b'1 - Basic'2277.030504.670.28206.15224.50124.5886.3599.02
2419901981b'3 - BSc/MSc'1099.041-16.990.99182.4843.35311.1735.3428.34
3519861973b'3 - BSc/MSc'1763.04135.230.90338.6247.80182.5918.7841.45
4619861956b'2 - High School'2566.041-24.331.00440.7518.90114.807.007.67
5719791943b'2 - High School'4103.040-66.011.05156.92295.60317.9514.6726.34
6819881974b'2 - High School'1743.041-144.911.13248.27397.30144.3666.6853.23
7919811978b'3 - BSc/MSc'1862.011356.530.36344.5118.35210.048.789.89
81019761948b'3 - BSc/MSc'3842.010-119.351.12209.26182.25271.9439.2355.12
91119901945b'3 - BSc/MSc'3995.040290.170.53296.50116.70227.7118.6710.89

Last rows

CustIDFirstPolYearBirthYearEducDegMonthSalGeoLivAreaChildrenCustMonValClaimsRatePremMotorPremHouseholdPremHealthPremLifePremWork
98401028519801987b'3 - BSc/MSc'1504.041-1.550.96390.6329.45179.70-6.0025.67
98411028619851948b'3 - BSc/MSc'3878.041-57.451.04269.05217.25219.9332.4525.67
98421028719971943b'3 - BSc/MSc'3975.020220.270.62285.6177.25241.4931.458.89
98431028819961941b'2 - High School'3845.04099.470.9087.35843.50121.58157.9233.45
98441028919821993b'2 - High School'1465.011795.150.3567.79820.15102.13182.4886.46
98451029019861943b'2 - High School'3498.040245.600.67227.82270.60160.92100.1369.90
98461029219841949b'4 - PhD'3188.020-0.110.96393.7449.45173.819.7814.78
98471029419941976b'3 - BSc/MSc'2918.011524.100.21403.63132.80142.2512.674.89
98481029519811977b'1 - Basic'1971.021250.050.65188.59211.15198.3763.90112.91
98491029619901981b'4 - PhD'2815.011463.750.27414.0894.45141.256.8912.89